gpt2-conversational-retrain

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This a fine-tuned version of gpt2 on Locutusque/InstructMix.

Model Details

This model performs significantly better than Locutusque/gpt2-conversational-or-qa. Here are the training results:

• BLEU - 26
• Perplexity - 12

Model Description

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• Developed by: Locutusque
• Shared by [optional]: [More Information Needed]
• Model type: GPT-2
• Language(s) (NLP): English
• License: [More Information Needed]
• Finetuned from model [optional]: GPT-2

Model Sources [optional]

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• Demo [optional]: [More Information Needed]

Uses

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This model is designed to follow instructions, or partake in conversations.

Direct Use

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Instruction-following or conversational.

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('gpt2-conversational-retrain')
model = GPT2LMHeadModel.from_pretrained('gpt2-conversational-retrain')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def generate_text(model, tokenizer, prompt, max_length=1024):
    prompt = f'<|USER|> {prompt} <|ASSISTANT|> '
    input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device)
    attention_mask = torch.ones_like(input_ids).to(device)
    output = model.generate(input_ids, 
                            max_length=max_length, 
                            do_sample=True,
                            temperature=0.3, 
                            top_k=23, 
                            top_p=0.7,
                            repetition_penalty=1.176,
                            pad_token_id=tokenizer.pad_token_id,
                            eos_token_id=tokenizer.eos_token_id,
                            attention_mask=attention_mask)
    output_ids = tokenizer.decode(output[0], skip_special_tokens=False)
    return output_ids
# Loop to interact with the model
while True:
    prompt = input("Enter a prompt (or 'q' to quit): ")
    if prompt == "q":
        break
    output_text = generate_text(model, tokenizer, prompt)
    print(output_text)

Training Details

Training Data

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https://huggingface.co/datasets/Locutusque/InstructMix

This model has so far been trained on 10% of the linked data, with more training sessions to come.

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: fp16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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